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dBFS is a digital signal measurement, relative to full-scale. dBSPL is a sound pressure level measurement, relative to 20 μPa RMS air pressure. dB(A) is shorthand for "dBSPL A-weighted", which is the same dBSPL measurement after applying an A-weighting filter. You're going to have to thoroughly understand these concepts before you can convert between them. ...


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To make it sound natural you typically introduce some small random pitch, speed & gain modulations. That's not a trivial amount of work, especially if you want something that sounds natural and good and maintains the original phrasing. This is a pretty common plug-in in audio processing. It's typically called "vocal doubler" or "voice doubler" and ...


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In a way, you have answered this already with: FastICA suppose that we have as many sources as channels, but does not check it in any way. If I am going to run the algorithm on my data, it would extract two sources and a 2x2 mixing matrix. ICA will indeed provide a separation, along the lines of section 3 ("What is independence?") which would also ...


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First of all, I wouldn't worry too much about the speaker response since it is relatively flat and the microphone has a much bigger roll-off. Since you've captured the frequency response using sweep, why not to skip the whole part of designing the filter that mimics the frequency response and use the original impulse response? I don't know what kind of ...


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First, the original $x$ and reconstructed $\hat{x}$ signals have a peak amplitude around $0.35$. Their difference peaks above $0.40$. That happens between two time-shifted like-alike signals. Second, the difference seems to relate to the amplitude. Processing maybe be non-linear, and it could be interesting to look at relative differences like $2(x -\hat{x}...


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